Pharmacogenomics in Drug Development

1. Pharmacogenomics in Drug Development FDA Science Board April 9, 2003 Brian B. Spear, Ph.D. Director, Pharmacogenomics Abbott Laboratories

2. Presentation Overview • Current application of pharmacogenomics in drug development • Industry concerns regarding the conduct of pharmacogenomic studies and submission of pharmacogenomic data

3. Current Applications of Pharmacogenomics • Primary uses relate to interpretation of clinical trial results, data quality, study design, and biomarkers. • Targeting drugs at genetically-defined populations is not a primary focus in pharmaceutical development • Three areas of greatest activity • Clinical genotyping • Pre-clinical gene expression • Clinical gene expression

4. Clinical Pharmacogenetics • Phase I studies • Explain outliers or patient-to-patient variability in PK • Exclude or include specific patients • Normalize genotype frequencies • Bridge to other populations

5. Example:Desipramine PK Parameters Genotyping can increase trial safety and explain outlying data • Drug interaction study • CYP2D6 poor metabolizers (2 null alleles) excluded. • One outlier with slow metabolism • Outlier has *6 null allele and *9 allele with reduced enzymatic activity. • Expected occurrence of null/*9 genotype is 0.4% 50 CYP2D6 *6/*9 40 t1/2, hr 30 20 10 Katz et al., Abbott Labs.

6. Clinical Pharmacogenetics • Phase II/III studies • Identify genetically-defined groups with more pronounced or rapidly progressing disease • Exclude/include at-risk individuals • Stratify studies based on genotypes • Clinical response • Risk of adverse events • Where appropriate, develop drugs for specific groups • Identify genetic markers associated with clinical outcomes

7. Example:Gene Association using SNP Mapping Association of anonymous SNP markers with Alzheimer’s Disease APOe Courtesy Allen Roses, GSK Similar methods can be used to identify genes associated with drug effect or drug adverse reactions

8. Pre-clinical Gene Expression • Toxicogenomics • Predict toxicity of candidate compounds • Identify mechanisms of toxicity • Identify potential biomarkers for toxicity or efficacy for future clinical studies

9. Example:Toxicogenomics of Hepatotoxic Compounds 52 compounds – rat liver mRNA analyzed by microarray Waring, et al., Abbott

10. Gene Expression Studies Can Produce Enormous Amounts of Data Page 2 of 28 Page Document Statistical methods are being developed for interpretation Gene p-value

11. Clinical Gene Expression • Biomarkers for drug response • Biomarkers for drug-induced toxicity • Comparison of human response to pre-clinical animal models • Identify genes with variants that may define patient populations • Identify proteins as potential biomarkers

12. Example:Gene Expression in Clinical TrialsResponse to Cyclosporin and rhIL11 in Psoriasis • Evaluation of >7000 genes in microarray • 159 found to associate with psoriasis • 142 found to associate with improvement of psoriatic skin in response to therapeutic agents • Gene expression reflects drug response Non-Responder Responder Avg. PSI 9 8.7 5.8 4.1 5.6 Avg. PSI 9.5 9.5 8 9 9 1 10 Fold Change (lesion/treatment) Fold Change (lesion/treatment) 1 0.1 0.1 0 1 4 8 12 0 1 4 8 12 Treatment Week Treatment Week Andrew J. Dorner Molecular Medicine, Wyeth Self-organizing map analysis of drug response for psoriasis-related genes

13. The Challenge in High-Density Genomic Analyses • Modern micro-array and whole genome analyses can generate tens of thousands of data points • Analysis is dependent upon statistical methods which are themselves experimental • No clear methods to determine validity of conclusions • Results can be subject to multiple interpretations • Genomics data and biological impact is incompletely understood

14. Submission of Data From Pharmacogenomic Studies • Drug developers are hesitant to initiate high-density pharmacogenomic studies and reluctant to share data with regulators • Analytical methods have not been developed to the point where valid conclusions can be drawn • Data can be subjected to multiple statistical methods • Reviewers might lack appropriate training or expertise • Results may be mis- or over-interpreted • Review may impact review timeline • These may lead to unfavorable regulatory impact and jeopardize a drug development program

15. Commentary on Preliminary FDA Proposal on Submission of Pharmacogenomic Data • Favorable aspects • Lowering of risk in conducting high-density pharmacogenomic studies • Evaluation by qualified experts • Consistent evaluation covering multiple compounds • Pharmacogenomics review independent of medical review timeline • Joint FDA/Industry effort to provide common basis for research exemption process

16. Commentary on Preliminary FDA Proposal on Submission of Pharmacogenomic Data • Uncertain aspects • Definition of “Pharmacogenomic Data” • Terms under which public health concerns would overrule research exemption • Process for feedback from FDA to companies • Unfavorable aspects • Possible future rescinding of research exemption • Potential requirement for additional studies for drug registration

17. Key Challenges in Pharmacogenomics Data submission is only one of many issues facing industry • Complexity of biological responses: genetics isn’t everything • Value of a pharmacogenomic study is often unknown until it has been completed • No clear regulatory pathway for pharmaco-genomics (including assays) • Financial constraints weigh against programs with uncertain outcomes

18. Unresolved Issues in Application of Pharmacogenomics • What are reasonable expectations of the role of genetics in drug responses? • If a relationship is identified between a genotype and a response, will that lead to specific labeling requirements, even if the drug is safe and effective for the general population? • Will collection of DNA in a clinical trial be a green light for the FDA to request pharmacogenetic studies?

19. Unresolved Issues in Application of Pharmacogenomics • Will the division of the patient population into multiple genetic subgroups lead to a request for larger studies to enable statistical power for each group? • What will be the regulatory requirements for tests indicated on the drug label (IVD vs homebrew) and for tests used in genotyping for registrational studies? • Under what conditions will it be possible to label a drug based on testing of only a pharmacogenetically defined patient group?

20. Conclusions • Pharmacogenomics is becoming an integral part of drug discovery and development • Excellent progress is being made in cooperative programs between industry and the FDA • Clarity in the FDA’s expectations of pharmacogenomics will encourage the use of these new technologies